Me, Graphically

Loading Packages

library(rmdformats)
library(tidyverse)
library(zoo)
library(plotly)

Loading the dataset

movies <- read_csv("movies.csv", show_col_types = FALSE)
head(movies)

## # A tibble: 6 × 5
##   Movie          `Date Watched` Genre  `Rating (out of 5)` Length
##   <chr>          <chr>          <chr>                <dbl>  <dbl>
## 1 13 Going on 30 8/24/2023      Comedy                 3.5     98
## 2 Wrong Turn     8/26/2023      Horror                 3       84
## 3 Candy Man      8/27/2023      Horror                 4       91
## 4 Evil Dead Rise 8/29/2023      Horror                 2.5     97
## 5 Orphan         9/3/2023       Horror                 3      123
## 6 Gone Girl      9/4/2023       Drama                  5      145

Creating the plot

#Assigning colors for the different genres of movies
color_list <- c("#ef233c", "#fdc500", "#70e000", "#788bff" ,"#bc00dd", "#f72585")

#Formatting the 3 axes
x <- list(title = "Date Watched", #Setting the name of the axis
          gridcolor='white', #Setting the grid color
          zerolinecolor='white', #Setting the zero line color to match the grid color
          showbackground=TRUE, #Affirming that we want to see the background color we pick 
          backgroundcolor='#003049',# Setting the background color
          titlefont = list(size = 15, #axis title size
                           family = "Georgia", #axis title font
                           color = "white"),
          tickfont = list(size = 10, #tick font size
                          family = "Georgia", #tick font
                          color = "white")) #tick color

#Repeating the process for the y and z axes
y <- list(title = "Rating (out of 5)", gridcolor='white', zerolinecolor='white', showbackground=TRUE, backgroundcolor='#003049',
          titlefont = list(size = 15, family = "Georgia", color = "white"), 
          tickfont = list(size = 12, family = "Georgia", color = "white"))
z <- list(title = "Length (minutes)", gridcolor='white', zerolinecolor='white', showbackground=TRUE, backgroundcolor='#003049',
          titlefont = list(size = 15, family = "Georgia", color = "white"), 
          tickfont = list(size = 12, family = "Georgia", color = "white"))

#Creating the plot with plot_ly
fig <- plot_ly(movies, #data set to be used
               x = ~`Date Watched`, #x axis
               y = ~`Rating (out of 5)`, #y axis
               z = ~Length, #z axis
               color = ~Genre, #what the points will be colored by
               colors = color_list, #setting the list of colors to be used for the scatter points
               type = 'scatter3d', #type of plot
               alpha = 0.8, #opacity of the scatter points
               marker = list(symbol = "circle"), #setting the shape of the points
               text = ~paste('Movie:' = Movie) #Adding the movie title to the hovertext
               )
#Customizing the layout of the graph
fig <- fig %>% layout(title = list(text = "Movies Watched During the 2023-2024 School Year", #title text
                                   font = list(size = 25, #title size
                                               family = "Academy Engraved LET",
                                               color = "white")), #title font
                      margin = 0, #setting margins to 0
                      scene = list(xaxis = x, yaxis = y, zaxis = z), #applying the axes customizations generated earlier
                      paper_bgcolor = "#003049", #background color
                      legend = list(font = list(
                        family = "Georgia", #legend font
                        size = 12, #legend font size
                        color = "white"), #text color
                        bgcolor = "#003049", #background color
                        title = list(text = "Genre:"),
                        bordercolor = "white",
                        borderwidth = 2.5)) #legend title text
#Show the result! :)
fig

Design Process

When assigned the project of visualizing my identity through a graphic, I knew that I wanted to show more of who I am as a person rather than simply some aspect of my daily life. Movies have always served as a foundational aspect of who I am. My favorite films have influenced my style (clothing and home decor), the music I listen to, and even the books I read for pleasure. While, as a graduate student, I do not have nearly as much time to watch movies as I used to, I chose to chart out the movies I have watched this school year to showcase my preferences and changing tastes throughout the months. I chose to create a 3-dimensional graph with 3 axes: the date I watched the movie (x-axis), my rating of the movie from 1 to 5 (y-axis), and the length of the movie in minutes (z-axis). Additionally, the points in the graph are color-coded by the genre of the movie. I believe that adding length as a third dimension and color-coding my points adds more context to my rating of the movie as these features highlight my personal preferences. For example, while I do not have a single “drama” film rated below ⅗, I only have one romance movie logged with a score of only 2.5/5. These added features also reflect how my preferences for movies change with the seasons as well. Another example: I have a considerably larger number of horror movies logged during August through October because my favorite season is Halloween; in contrast, I watched longer action movies during the holiday season because I had more free time to do so during the winter holiday. One of my favorite features of this graph is the hover-text option built into the graph. Admittedly, viewing a 3d graph can be moderately overwhelming if one is not sure what they are supposed to be gathering from the visualization, but the hover-text provides the specific information of each point. When dragging the mouse over a point on the graph, the hover text displays the title of the movie along with the values for each axis associated with that point, giving more context to each piece of data. For the color scheme of the graph, I chose to use a dark blue-gray background to make the vibrancy of the scatter points “pop” against the darkness. This color choice also reminded me of the darkness of a movie theater in contrast to the vibrant colors on the screen, fitting the theme of the visualization well. I colored all of the text and graphical aspects in white to make them visible against the dark background, but not clash with the colors of the scatter points. An aspect I played safe for this assignment was my mode of composition. I created this graph in R-Studio because I was previously taught how to make 3d graphs in another class; I already knew the foundations of the code I needed to generate for my desired outcome. While I am excited to learn more about the tools we will use like Observable and Adobe Illustrator, I knew it would be “easier” to create my graph in Rstudio as I am very comfortable with the platform already. The biggest challenge for the assignment was implementing my stylistic choices and finding a color palette that I was satisfied with. This is primarily because I can be a somewhat indecisive person, but also because RStudio is a bit limited aesthetically. Figuring out how to format the colors of the background, change the ticks, and pick new fonts took a good amount of research online, as I had to write my code to specifically work for the program I was using within RStudio (plotly) AND for a 3d scatter plot at that. I am glad I had the patience to research how to format the graph exactly as I liked, and the result is almost identical to how I visualized the graph in my head.